Insights Accelerator
Transforming complex Python scripts into an intuitive tool for non-technical users
The Problem
How can we transform complex market analyses into a user-friendly interface for rapid consumer insight generation?
The Insights Accelerator began as a suite of Python scripts aimed at conducting common market analyses. While technically robust, the tool posed significant usability challenges:
• Confusing terminology that created a barrier to understanding
• Knowledge of Python was required, resulting in low adoption among non-technical users
Process
1. Interviewed both technical and non-technical users to understand their interactions with the existing tool and identify areas for improvement.
• Non-technical users found the Python scripts intimidating and hard to navigate
• Technical users stressed the importance of aligning the tool’s design with existing workflows for integrations
• Both types of users indicated a need for clearer terminology and a more intuitive interface to increase adoption
2. Outlined an information architecture that organized the top analyses into a tabbed interface with a linear flow, making it easier for users to navigate and stay on task.
Using insights from research interviews, we landed on a simple tabbed architecture with one key linear flow, running an analysis. This streamlined and focused structure enables users to navigate the tool effortlessly and remain on task.
3. Created wireframes for key screens and features to gather early feedback and explore different layout options.
4. Applied Deloitte's style guide for visual consistency.
5. Refined screens to develop high-fidelity mockups.
6. Conducted usability testing with users and iterated designs based on feedback.
• "I don't really trust the results. I'm not sure where the numbers come from" ➝ Added tips and expandable sections explaining how data is sourced and how results are calculated.
• "Some of the terminology is confusing. What does it actually do?" ➝ Renamed the tool "Insights Accelerator" and rewrote internal labels to reflect business value, not developer jargon.
Results
100%
Adoption among non-technical users post-launch.
3X
Reduction in time spent on conducting an analysis.
Enhanced Collaboration
Between technical and non-technical teams through a shared vocabulary.
Lessons learned
• Clear communication bridges technical divides: Simplifying terminology and aligning the tool's design with user expectations significantly improved accessibility.
• User involvement is key to successful design: Regular feedback loops with end-users ensured the final product met their needs and preferences.
